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AMT | Articles | Volume 12, issue 3
Atmos. Meas. Tech., 12, 1697-1716, 2019
https://doi.org/10.5194/amt-12-1697-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Atmos. Meas. Tech., 12, 1697-1716, 2019
https://doi.org/10.5194/amt-12-1697-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 18 Mar 2019

Research article | 18 Mar 2019

Retrieval of liquid water cloud properties from POLDER-3 measurements using a neural network ensemble approach

Antonio Di Noia et al.
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Cited articles  
Aires, F., Marquisseau, F., Prigent, C., and Sèze, G.: A land and ocean microwave cloud classification algorithm derived from AMSU-A and -B, trained using MSG-SEVIRI infrared and visible observations, Mon. Weather Rev., 139, 2347–2366, https://doi.org/10.1175/MWR-D-10-05012.1, 2011. a
Alexandrov, M. D., Cairns, B., Emde, C., Ackerman, A. S., and van Diedenhoven, B.: Accuracy assessments of cloud droplet size retrievals from polarized radiance measurements by the research scanning polarimeter, Remote Sens. Environ., 125, 92–111, https://doi.org/10.1016/j.rse.2012.07.012, 2012a. a
Alexandrov, M. D., Cairns, B., and Mishchenko, M. I.: Rainbow Fourier transform, J. Quant. Spectrosc. Ra., 113, 2521–2535, https://doi.org/10.1016/j.jqsrt.2012.03.025, 2012b. a
Arduini, R. F., Minnis, P., Smith Jr., W. L., Ayers, J. K., Khaiyer, M. M., and Heck, P.: Sensitivity of satellite-retrieved cloud properties to the effective variance of cloud droplet size distribution, in: Fifteenth ARM Science Team Meeting Proceedings, Daytona Beach, FL, USA, 14–18 March 2005, 2005. a
Baum, B. A., Soulen, P. F., Strabala, K. I., King, M. D., Ackerman, A. S., Menzel, W. P., and Yang, P.: Remote sensing of cloud properties using MODIS airborne simulator imagery during SUCCESS: 2. Cloud thermodynamic phase, J. Geophys. Res., 105, 11781–11792, https://doi.org/10.1029/1999JD901089, 2000. a
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We present a neural network algorithm for the retrieval of cloud physical properties from multi-angle polarimetric measurements. We have trained the algorithm on a large dataset of synthetic measurements and applied it to a year of POLDER-3 data. A comparison against MODIS cloud products reveals that our algorithm is capable of performing cloud property retrievals on a global scale and possibly improves the estimates of cloud effective radius over land with respect to existing POLDER-3 products.
We present a neural network algorithm for the retrieval of cloud physical properties from...
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